In the past few decades, the researchers proposed several computer-aided methods to detect AFib and other arrhythmias automatically. Several methods are proposed to enable automation. The primary task for arrhythmia detection is to categorize each heartbeat (typically consisting of a P wave, a QRS complex and a T wave) into a set of predefined classes. Some of them include Feature extraction approach [1]}, wavelet-based techniques [2]}, support vector machine-based techniques[3]}, hidden Markov model-based techniques [4]} and Machine learning (ML) models, and Deep Learning (DL) approaches [5]} [6]} [7]}. ML and DL models show promising results in classifying AFib. Models available in literature often failed to provide adequate sensitivity and specificity because of inter-patient variability, highly skewed class-wise performance and noise Etc. Therefore, an improved approach to segregate different heartbeats based on arrhythmia is the need for time.
